from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 14 predictions (58.33%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.945158	0.174170	NaN	0.000411	0.001945	brute	-1	1	0.663	0.173001	0.003892	0.687	11.243621	11.246466
4	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.759015	0.022848	NaN	0.000290	0.002759	brute	-1	5	0.757	0.176017	0.005928	0.742	15.674675	15.683563
7	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.060653	0.033896	NaN	0.000388	0.002061	brute	1	100	0.882	0.207801	0.001673	0.875	9.916474	9.916795
8	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.033698	0.013977	NaN	0.000024	0.033698	brute	1	100	1.000	0.008603	0.000278	0.000	3.916920	3.918967
10	KNeighborsClassifier_brute_force	predict	0.007	100000	1000	100	2.786869	0.043162	NaN	0.000287	0.002787	brute	-1	100	0.882	0.208362	0.000498	0.875	13.375151	13.375189
11	KNeighborsClassifier_brute_force	predict	1.000	100000	1	100	0.022921	0.002096	NaN	0.000035	0.022921	brute	-1	100	1.000	0.008707	0.000804	0.000	2.632306	2.643510
13	KNeighborsClassifier_brute_force	predict	0.015	100000	1000	100	2.021048	0.007167	NaN	0.000396	0.002021	brute	1	5	0.757	0.172025	0.000248	0.742	11.748590	11.748602
16	KNeighborsClassifier_brute_force	predict	0.024	100000	1000	100	1.160968	0.002671	NaN	0.000689	0.001161	brute	1	1	0.663	0.172713	0.000432	0.687	6.721949	6.721970
19	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.752716	0.023652	NaN	0.000009	0.001753	brute	-1	1	0.896	0.025569	0.000230	0.967	68.548700	68.551473
22	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	2.641044	0.014811	NaN	0.000006	0.002641	brute	-1	5	0.922	0.026547	0.000071	0.974	99.485543	99.485902
25	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	1.954314	0.002365	NaN	0.000008	0.001954	brute	1	100	0.929	0.059959	0.001892	0.975	32.594196	32.610421
28	KNeighborsClassifier_brute_force	predict	0.046	100000	1000	2	2.651842	0.023051	NaN	0.000006	0.002652	brute	-1	100	0.929	0.059574	0.000147	0.975	44.513100	44.513236
31	KNeighborsClassifier_brute_force	predict	0.052	100000	1000	2	1.932948	0.003562	NaN	0.000008	0.001933	brute	1	5	0.922	0.026996	0.000936	0.974	71.601608	71.644610
34	KNeighborsClassifier_brute_force	predict	0.071	100000	1000	2	1.060468	0.008314	NaN	0.000015	0.001060	brute	1	1	0.896	0.025551	0.000073	0.967	41.504331	41.504498

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.262	0.0	-1	1	0.050	0.005	0.221	0.222	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.240	0.0	-1	5	0.048	0.001	0.230	0.230	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.233	0.0	1	100	0.048	0.000	0.230	0.230	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.014	0.005	5.749	0.0	-1	100	0.048	0.001	0.292	0.292	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.185	0.0	1	5	0.048	0.000	0.231	0.231	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.163	0.0	1	1	0.049	0.000	0.230	0.230	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.372	0.0	-1	1	0.008	0.000	0.524	0.524	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.376	0.0	-1	5	0.008	0.000	0.509	0.509	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.377	0.0	1	100	0.008	0.000	0.513	0.513	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.378	0.0	-1	100	0.008	0.000	0.511	0.511	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.377	0.0	1	5	0.008	0.000	0.506	0.506	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.375	0.0	1	1	0.008	0.000	0.516	0.516	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.945	0.174	0.000	0.002	-1	1	0.173	0.004	11.244	11.246	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	0.000	0.023	-1	1	0.008	0.000	2.701	2.701	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.759	0.023	0.000	0.003	-1	5	0.176	0.006	15.675	15.684	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.003	0.000	0.023	-1	5	0.008	0.000	2.781	2.782	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.061	0.034	0.000	0.002	1	100	0.208	0.002	9.916	9.917	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.034	0.014	0.000	0.034	1	100	0.009	0.000	3.917	3.919	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.787	0.043	0.000	0.003	-1	100	0.208	0.000	13.375	13.375	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	100	0.009	0.001	2.632	2.644	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.021	0.007	0.000	0.002	1	5	0.172	0.000	11.749	11.749	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	5	0.009	0.000	2.233	2.235	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.161	0.003	0.001	0.001	1	1	0.173	0.000	6.722	6.722	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.002	0.000	0.019	1	1	0.009	0.000	2.267	2.269	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.753	0.024	0.000	0.002	-1	1	0.026	0.000	68.549	68.551	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.006	0.004	0.000	0.006	-1	1	0.001	0.000	9.260	9.280	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.641	0.015	0.000	0.003	-1	5	0.027	0.000	99.486	99.486	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.005	0.000	0.007	-1	5	0.001	0.000	11.634	11.664	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.954	0.002	0.000	0.002	1	100	0.060	0.002	32.594	32.610	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	4.107	4.119	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.652	0.023	0.000	0.003	-1	100	0.060	0.000	44.513	44.513	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.008	0.002	0.000	0.008	-1	100	0.001	0.000	10.810	10.864	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.933	0.004	0.000	0.002	1	5	0.027	0.001	71.602	71.645	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.528	4.546	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.060	0.008	0.000	0.001	1	1	0.026	0.000	41.504	41.504	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.876	2.888	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 12 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.846699	1.024024	NaN	0.000094	0.000847	kd_tree	-1	1	0.929	0.125995	0.003272	0.910	6.720102	6.722368
4	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.084310	0.367576	NaN	0.000074	0.001084	kd_tree	-1	5	0.946	0.213829	0.003646	0.941	5.070915	5.071652
7	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	5.655300	0.495737	NaN	0.000014	0.005655	kd_tree	1	100	0.951	0.649424	0.015273	0.940	8.708174	8.710582
10	KNeighborsClassifier_kd_tree	predict	0.011	1000000	1000	10	3.315664	0.347322	NaN	0.000024	0.003316	kd_tree	-1	100	0.951	0.637073	0.010695	0.940	5.204530	5.205263
13	KNeighborsClassifier_kd_tree	predict	0.005	1000000	1000	10	1.803258	0.411965	NaN	0.000044	0.001803	kd_tree	1	5	0.946	0.223114	0.003650	0.941	8.082243	8.083324
16	KNeighborsClassifier_kd_tree	predict	0.019	1000000	1000	10	0.927696	0.169602	NaN	0.000086	0.000928	kd_tree	1	1	0.929	0.119940	0.001786	0.910	7.734659	7.735517
19	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.026077	0.013312	NaN	0.000614	0.000026	kd_tree	-1	1	0.891	0.000398	0.000045	0.879	65.453182	65.863322
22	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.023598	0.000617	NaN	0.000678	0.000024	kd_tree	-1	5	0.911	0.000641	0.000025	0.905	36.819075	36.847445
25	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.034779	0.004690	NaN	0.000460	0.000035	kd_tree	1	100	0.894	0.004482	0.000028	0.917	7.760221	7.760376
28	KNeighborsClassifier_kd_tree	predict	0.023	1000	1000	2	0.037834	0.003823	NaN	0.000423	0.000038	kd_tree	-1	100	0.894	0.005160	0.001467	0.917	7.332740	7.623220
31	KNeighborsClassifier_kd_tree	predict	0.006	1000	1000	2	0.020110	0.000135	NaN	0.000796	0.000020	kd_tree	1	5	0.911	0.000642	0.000031	0.905	31.317454	31.353569
34	KNeighborsClassifier_kd_tree	predict	0.012	1000	1000	2	0.018861	0.000117	NaN	0.000848	0.000019	kd_tree	1	1	0.891	0.000387	0.000025	0.879	48.782272	48.882440

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.947	0.034	0.027	0.0	-1	1	0.818	0.091	3.604	3.626	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.620	0.053	0.022	0.0	-1	5	0.766	0.013	4.725	4.726	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.718	0.060	0.022	0.0	1	100	0.759	0.004	4.897	4.897	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	4.086	0.072	0.020	0.0	-1	100	0.755	0.010	5.411	5.412	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.800	0.048	0.021	0.0	1	5	0.771	0.011	4.929	4.929	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.860	0.070	0.021	0.0	1	1	0.757	0.015	5.100	5.100	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.016	0.0	-1	1	0.003	0.002	0.332	0.374	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	5	0.002	0.002	0.233	0.296	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	100	0.001	0.001	0.457	0.555	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	-1	100	0.001	0.000	0.617	0.618	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	5	0.001	0.000	0.623	0.625	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.031	0.0	1	1	0.001	0.000	0.664	0.664	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.847	1.024	0.000	0.001	-1	1	0.126	0.003	6.720	6.722	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.000	0.000	0.003	-1	1	0.000	0.000	9.686	10.154	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.084	0.368	0.000	0.001	-1	5	0.214	0.004	5.071	5.072	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	8.103	8.660	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.655	0.496	0.000	0.006	1	100	0.649	0.015	8.708	8.711	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	4.549	4.867	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.316	0.347	0.000	0.003	-1	100	0.637	0.011	5.205	5.205	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.006	0.001	0.000	0.006	-1	100	0.001	0.000	7.852	8.362	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.803	0.412	0.000	0.002	1	5	0.223	0.004	8.082	8.083	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.000	0.000	0.002	1	5	0.000	0.000	3.879	4.167	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.928	0.170	0.000	0.001	1	1	0.120	0.002	7.735	7.736	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	3.760	4.063	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.026	0.013	0.001	0.000	-1	1	0.000	0.000	65.453	65.863	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	27.003	28.846	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.001	0.001	0.000	-1	5	0.001	0.000	36.819	36.847	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	26.460	28.184	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.035	0.005	0.000	0.000	1	100	0.004	0.000	7.760	7.760	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	6.344	6.701	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.004	0.000	0.000	-1	100	0.005	0.001	7.333	7.623	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	23.682	25.112	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	5	0.001	0.000	31.317	31.354	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.486	6.847	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.019	0.000	0.001	0.000	1	1	0.000	0.000	48.782	48.882	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.646	6.986	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.522	0.068	30	0.031	0.0	random	0.387	0.026	1.347	1.350	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.573	0.015	30	0.028	0.0	k-means++	0.415	0.025	1.380	1.383	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.674	0.117	30	0.141	0.0	random	2.730	0.021	2.078	2.078	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.975	0.075	30	0.134	0.0	k-means++	2.940	0.042	2.033	2.033	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.009	0.000	random	0.0	0.0	12.564	15.117	See	See
2	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	random	0.0	0.0	8.409	14.199	See	See
4	KMeans_tall	predict	1000000	1000	2	0.001	0.000	30	0.011	0.000	k-means++	0.0	0.0	10.551	12.232	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	14.770	15.672	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.398	0.000	random	0.0	0.0	8.033	8.745	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	14.183	14.639	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.474	0.000	k-means++	0.0	0.0	6.212	6.720	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	14.053	14.534	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 4 predictions (50.0%).
The observed differences can be found in the diff_adjusted_rand_scores column.
The chosen difference threshold is 0.001 for adjusted_rand_score.
See details in the dataframe below.

	estimator	function	diff_adjusted_rand_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup
1	KMeans_short	predict	0.001090	10000	1000	2	0.001832	0.000171	20	0.008733	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000439	0.000045	-0.000965	4.169565	4.191795
4	KMeans_short	predict	0.001995	10000	1000	2	0.001836	0.000140	20	0.008715	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000439	0.000055	-0.000750	4.183997	4.217065
7	KMeans_short	predict	0.015034	10000	1000	100	0.002504	0.000215	20	0.319429	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.000941	0.000091	0.293767	2.661817	2.674256
10	KMeans_short	predict	0.060044	10000	1000	100	0.002421	0.000147	20	0.330457	0.000002	full	k-means++	20	300	1	1.000000e-16	0.317011	0.000947	0.000111	0.256968	2.555925	2.573268

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.075	0.001	20	0.002	0.0	random	0.026	0.002	2.882	2.888	See	See
3	KMeans_short	fit	10000	10000	2	0.218	0.003	20	0.001	0.0	k-means++	0.080	0.000	2.705	2.705	See	See
6	KMeans_short	fit	10000	10000	100	0.199	0.003	20	0.040	0.0	random	0.107	0.002	1.853	1.854	See	See
9	KMeans_short	fit	10000	10000	100	0.568	0.015	20	0.014	0.0	k-means++	0.295	0.003	1.925	1.926	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.000	0.0	4.170	4.192	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	14.039	14.482	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.000	0.0	4.184	4.217	See	See
5	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	0.000	0.0	12.624	13.016	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.319	0.000	random	0.001	0.0	2.662	2.674	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	10.472	10.782	See	See
10	KMeans_short	predict	10000	1000	100	0.002	0.0	20	0.330	0.000	k-means++	0.001	0.0	2.556	2.573	See	See
11	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	0.000	0.0	9.763	10.048	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 2 predictions (50.0%).
The observed differences can be found in the diff_accuracy_scores column.
The chosen difference threshold is 0.001 for accuracy_score.
See details in the dataframe below.

	estimator	function	diff_accuracy_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup
1	LogisticRegression	predict	0.01	1000000	1000	100	0.000395	0.000467	[20]	2.026996	3.946728e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000810	0.001551	0.55	0.486988	1.051396
4	LogisticRegression	predict	0.07	1000	100	10000	0.001595	0.000377	[26]	5.017054	1.594561e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.005511	0.000775	0.28	0.289352	0.292203

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.187	0.433	[20]	0.072	0.000	2.021	0.011	5.534	5.534	See	See
3	LogisticRegression	fit	1000	1000	10000	0.994	0.603	[26]	0.081	0.001	1.002	0.032	0.991	0.992	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.027	0.0	0.001	0.002	0.487	1.051	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.016	0.0	0.000	0.000	0.368	0.374	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	5.017	0.0	0.006	0.001	0.289	0.292	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.129	0.0	0.002	0.000	0.045	0.045	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

Mismatches between validation scores

WARNING! Mismatch between validation scores for 1 prediction (25.0%).
The observed differences can be found in the diff_r2_scores column.
The chosen difference threshold is 0.001 for r2_score.
See details in the dataframe below.

	estimator	function	diff_r2_scores	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup
1	Ridge	predict	0.039624	1000	1000	10000	0.01181	0.000205	NaN	6.773758	0.000012	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.019658	0.000445	0.122191	0.600778	0.600931

Raw results

fit

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.181	0.012	0.443	0.0	0.184	0.002	0.981	0.981	See	See
3	Ridge	fit	1000000	1000000	100	1.176	0.096	0.680	0.0	0.311	0.247	3.781	4.828	See	See

predict

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.012	0.0	6.774	0.0	0.02	0.0	0.601	0.601	See	See
2	Ridge	predict	1000	1	10000	0.000	0.0	1.354	0.0	0.00	0.0	0.650	0.717	See	See
4	Ridge	predict	1000000	1000	100	0.000	0.0	5.310	0.0	0.00	0.0	0.566	0.800	See	See
5	Ridge	predict	1000000	1	100	0.000	0.0	0.016	0.0	0.00	0.0	0.651	0.685	See	See